2D Human Pose Estimation
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What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xhujoy/CycleGAN-tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/xiadanqing/Binary
https://github.com/xiangyue9607/CliniRC
https://github.com/xiaozeguo123/-Quick-Capture-system-HSR
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/xidexia/realtime_PST
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
https://github.com/xikai97/med-mim
https://github.com/xinntao/BasicSR
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/xinyuanc91/Gated-GAN
Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer
https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/xiuyu0000/papers_with_examples/tree/main/arbitrary_image_stylization
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/xiuyu0000/papers_with_examples/tree/main/cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/xiuyu0000/papers_with_examples/tree/main/reconet
https://github.com/xn1997/HigherHRnet
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
https://github.com/xoraus/Super-Resolution-CNN-for-Image-Restoration
https://github.com/xuberance137/styleflow
https://github.com/xuduo35/MakeLongVideo
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
https://github.com/xw-hu/Mask-ShadowGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/xxlya/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/xxxNARUTO228xxx/Neural-Style-Transfer-Gatys-LapStyle
https://github.com/yakhyo/Fast-Neural-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/yanggeng1995/vae_tacotron
Learning latent representations for style control and transfer in end-to-end speech synthesis
https://github.com/yangyucheng000/AlphaPose
https://github.com/yangyucheng000/CycleGan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/yangyucheng000/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/yangyucheng000/Paper-2/tree/main/WS-VAD-mindspore-main
https://github.com/yangyucheng000/Pix2pix-2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/yangyucheng000/ReCoNet
https://github.com/yangyucheng000/arbitrary_image_stylization
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/yangyucheng000/ascend_ArbitraryStyleTransfer
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/yanyangbaobeiIsEmma/Reinforcement-Learning-Contextual-Bandits
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/yardenfren1996/B-LoRA
https://github.com/yaswanthpalaghat/Implementing-Neural-Style-Transfer
https://github.com/ycjing/Character-Stylization
https://github.com/yesidc/ProjectANNsWTF
https://github.com/yex666/cyclegan-in-mindspore
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.